Reliably detecting the presence or absence of an object, such as a train in railway interchange environments such as station platforms, and on tracks in train and subway stations is desirable for railway system management. Automated video systems may be used to monitor pluralities of such railway interchange environments from a remote, centralized point, enabling efficiencies in centralizing and providing comprehensive and contextual train traffic management. Such systems may be useful in alerting train system managers to collision and pedestrian hazards posed by moving trains, for example by providing advance warnings to train operators or other parties remote from a station stop that another train is unexpectedly occupying or entering a station platform area, early enough to enable avoidance measures. Alarms from such systems may notify a train engineer to begin stopping a train early enough, so that its inertia may be safely overcome prior to entry of a problematic area.
However, video monitoring systems suffer from a variety of limitations. For example, the capabilities of such systems may be limited by reliance on human perception to both review video feeds of pluralities of different station areas, and make the necessary determinations to spot and abate problems in a timely fashion. The number of personnel available to watch video footage from vast camera arrays is generally limited by budgetary and other resource limitations, as is the ability of any one human monitor to monitor and perceive a threat in multiple, simultaneous video feeds. The process of watching video streams is resource consuming, suffers from high costs of employing security personnel, and efficiency in such systems to detect events of interest is also limited by the constraints of human comprehension.
The field of intelligent visual surveillance seeks to address this problem by applying computer vision techniques to video stream inputs to automatically detect the presence or absence of trains at given track locations. However, the efficacy of such systems in real-world conditions is limited. Accurately determining the presence or absence of a train at a given train platform area may be challenging, in one aspect due to a large variability in amounts of reflect light generated by changing weather conditions (sunny, cloudy, nighttime, transient moving cloud shadows, etc.). A wide variety of reflected or occluded lighting profiles must be processed that may be caused by different train cars, the numbers of train cars, speeds or movement relative to the video cameras, and the different sizes, shapes and reflective behaviors of the train car element surfaces. Strong visual textures are generally observed in rail track areas, and even a minor change in lighting may cause incorrect foreground classification in video analytic systems. Thus, high rates of false positive detections, or low rates of accuracy in detecting true events, generally limit the usefulness and trustworthiness of such systems.